3 papers
cs.LG2025
VS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing
Hamed Poursiami, Shay Snyder, Guojing Cong +2
Graph classification is a fundamental task in domains ranging from molecular property prediction to materials design. While graph neural networks (GNNs) achieve strong performance…
cs.LG2025
HyperGraphX: Graph Transductive Learning with Hyperdimensional Computing and Message Passing
Guojing Cong, Tom Potok, Hamed Poursiami +1
We present a novel algorithm, \hdgc, that marries graph convolution with binding and bundling operations in hyperdimensional computing for transductive graph learning. For predicti…
cs.LG2024
Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction
Avishek Bose, Guojing Cong
Graph neural networks (GNNs) have emerged as one of the most effective ML techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN mo…